Predictive Models To Determine Market Timing Opportunities For the Jse
J N Keuler and
J D Krige
Studies in Economics and Econometrics, 2009, vol. 33, issue 1, 59-83
Abstract:
The objective of this study is to establish whether it is possible to develop a mathematical forecasting model which can be used to out-perform the JSE All Share Index (ALSI) by switching between the ALSI and cash on a monthly basis.A number of models were formulated, using regression analysis to determine a future value and then transforming this predicted value via logit scaling to a probability that the ALSI will outperform cash in the future period. Based on this probability one of two decisions was made at the end of each month, that is to stay in the current asset or to switch to the alternate asset.The best results from these models outperformed the ALSI (dividends included) by 7 – 9 % compound per year over 15 years. The inclusion of transaction costs reduced the gain to 4 – 5 % compound per year. These results were better than the performance of both a random model and the ALSI by a statistically significant margin. Furthermore, these results compare very favourably with similar international studies which have been conducted during the past 10 years.
Date: 2009
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/10800379.2009.12106463 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:rseexx:v:33:y:2009:i:1:p:59-83
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/rsee20
DOI: 10.1080/10800379.2009.12106463
Access Statistics for this article
Studies in Economics and Econometrics is currently edited by Willem Bester
More articles in Studies in Economics and Econometrics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().